This course will provide students with an overview of tools for the analysis of test data. They will learn to understand and apply these tools using statistical software. During the course, students will work on the analysis of empirical data and make exercises about the theory. A more general aim is to enhance the student’s psychometric research skills. In psychology and education, attributes of individuals are often measured with tests. A test consists of a number of separate items, questions or problems to be solved. The responses are used to obtain a score that indicates the degree to which a person possesses a certain quality, e.g. compulsiveness or spatial intelligence. Psychologists are interested in various aspects of the scores of such tests. In particular they want to know something about its meaning, reliability, validity, and the best way to obtain such a score. To this end statistical theories for tests and measurements have been developed. In this course you will learn to understand the main test theories and to apply them. Substantive issues are only cursorily discussed; this is primarily an applied statistics course. The course has two parts: Part I deals with traditional test theory, Part II with modern test theory. The former is most often used, but the latter is much more powerful and elegant and has a usefulness that goes far beyond that of traditional test theory. It also provides great research opportunities for students. Some more advanced applications of modern test theory are discussed at the end of the Part II. All computations and simulations will be performed with R..
McDonald, R.P. (1999) Test Theory: A unified Treatment, London: Lawrence Erlbau,m.